Evaluation of damage in giant cell arteritis
Bibliographic record
Abstract
Objectives: To evaluate damage and variables associated with damage in GCA. Methods: Patients with GCA enrolled in a prospective, multicentre, longitudinal study were included. Per-protocol assessments were made with the Vasculitis Damage Index and the Large-Vessel Vasculitis Index of Damage. Results: The study included 204 patients: 156 women (76%), mean age at diagnosis 71.3 years (s.d. 8.3), mean follow-up of 3.5 years (s.d. 1.9). One or more damage item was present in 54% at baseline and 79% at the last follow-up on the Vasculitis Damage Index, and 60% at baseline and 82% at the last follow-up on the Large-Vessel Vasculitis Index of Damage. The most frequently observed damage items were large-artery complications (29% cohort) and ocular (22%). Among 117 patients with new damage, most new items were ocular (63 patients), cardiac/vascular (48) and musculoskeletal (34). Of these, treatment-associated items were frequently observed, including cataracts (46 patients), osteoporosis (22) and weight gain (22). Disease-associated new damage included ischaemic optic neuropathy (3 patients), limb claudication (13), arterial occlusions (10) and damage requiring vascular intervention (10). In univariate analysis, the risk of damage increased 22% for every additional year of disease duration [odds ratio (OR) 1.22 (95% CI 1.04, 1.45)]. In 94 patients enrolled within ⩽90 days of diagnosis of GCA, the risk of new damage at the last follow-up decreased 30% for each additional relapse [OR 0.70 (95% CI 0.51, 0.97)]. Conclusions: Large-artery complications and ocular manifestations are the most commonly occurring items of damage in GCA. Most new damage is associated with treatment. These findings emphasize the cumulative burden of disease in GCA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".